Brain tissue segmentation method based on maximum between-cluster variance optimized by the difference search algorithm
10.3760/cma.j.issn.1673-4181.2019.05.009
- VernacularTitle: 基于差分搜索优化最大类间方差的脑组织分割方法
- Author:
Shuo WANG
1
;
Chunrong XU
;
Yan XIANG
;
Dangguo SHAO
;
Lijun LIU
;
Li ZHANG
Author Information
1. Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Publication Type:Journal Article
- Keywords:
Magnetic resonance imaging;
Differential search;
Image segmentation;
Multi threshold;
OTSU
- From:
International Journal of Biomedical Engineering
2019;42(5):409-413,440
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To study a maximum between-cluster variance based on differential search algorithm, and to select the multi-threshold for effectively segmentation of brain magnetic resonance images.
Methods:The brain extraction tool(BET) algorithm was used to remove the non-brain tissue part of the original magnetic resonance image. The best-fit with coalescing(BFC) algorithm was used to remove the intensity non-uniformity. The differential search algorithm was used to optimize the maximum between-cluster variance of the image to find the optimal threshold for multi-threshold segmentation of the magnetic resonance image. The method was validated using simulated magnetic resonance(MR) brain image data provided by BrainWeb.
Results:For MR images with different noise levels and intensity inhomogeneities, the proposed method was better than FSL, SPM and Brainsuite methods.
Conclusions:The maximum between-cluster variance based on differential search algorithm has better segmentation accuracy and robustness, especially for cerebrospinal fluid.
